Overview

Dataset statistics

Number of variables16
Number of observations2775
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory347.0 KiB
Average record size in memory128.0 B

Variable types

Numeric16

Alerts

gross_revenue is highly correlated with qnt_purchases and 3 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 2 other fieldsHigh correlation
var_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qnt_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with avg_basket_varietyHigh correlation
avg_recency_days is highly correlated with freq_purchaseHigh correlation
freq_purchase is highly correlated with avg_recency_daysHigh correlation
qtd_returned is highly correlated with freq_returns and 2 other fieldsHigh correlation
freq_returns is highly correlated with qtd_returned and 2 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_basket_variety is highly correlated with var_products and 1 other fieldsHigh correlation
item_rp_ratio is highly correlated with qtd_returned and 2 other fieldsHigh correlation
net_margin is highly correlated with qtd_returned and 2 other fieldsHigh correlation
gross_revenue is highly correlated with qnt_purchases and 1 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 2 other fieldsHigh correlation
var_products is highly correlated with qnt_purchasesHigh correlation
qnt_items is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_basket_size is highly correlated with qnt_itemsHigh correlation
item_rp_ratio is highly correlated with net_marginHigh correlation
net_margin is highly correlated with item_rp_ratioHigh correlation
gross_revenue is highly correlated with qnt_purchases and 1 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 1 other fieldsHigh correlation
var_products is highly correlated with avg_basket_varietyHigh correlation
qnt_items is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with freq_purchaseHigh correlation
freq_purchase is highly correlated with avg_recency_daysHigh correlation
qtd_returned is highly correlated with freq_returns and 2 other fieldsHigh correlation
freq_returns is highly correlated with qtd_returned and 2 other fieldsHigh correlation
avg_basket_size is highly correlated with qnt_itemsHigh correlation
avg_basket_variety is highly correlated with var_productsHigh correlation
item_rp_ratio is highly correlated with qtd_returned and 2 other fieldsHigh correlation
net_margin is highly correlated with qtd_returned and 2 other fieldsHigh correlation
df_index is highly correlated with avg_recency_daysHigh correlation
gross_revenue is highly correlated with qnt_purchases and 4 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 4 other fieldsHigh correlation
var_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qnt_items is highly correlated with gross_revenue and 4 other fieldsHigh correlation
avg_ticket is highly correlated with qtd_returnedHigh correlation
avg_recency_days is highly correlated with df_indexHigh correlation
qtd_returned is highly correlated with gross_revenue and 4 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 2 other fieldsHigh correlation
item_rp_ratio is highly correlated with net_marginHigh correlation
net_margin is highly correlated with item_rp_ratioHigh correlation
freq_purchase is highly skewed (γ1 = 46.09420632) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 33 (1.2%) zeros Zeros
qtd_returned has 1484 (53.5%) zeros Zeros
freq_returns has 1484 (53.5%) zeros Zeros
item_rp_ratio has 1484 (53.5%) zeros Zeros

Reproduction

Analysis started2021-10-23 02:41:17.011759
Analysis finished2021-10-23 02:41:41.941819
Duration24.93 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct2775
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2251.409369
Minimum0
Maximum5698
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-22T23:41:42.005806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile179.7
Q1901
median2060
Q33412
95-th percentile4961.3
Maximum5698
Range5698
Interquartile range (IQR)2511

Descriptive statistics

Standard deviation1527.723888
Coefficient of variation (CV)0.678563352
Kurtosis-0.9562554739
Mean2251.409369
Median Absolute Deviation (MAD)1241
Skewness0.3794131209
Sum6247661
Variance2333940.279
MonotonicityStrictly increasing
2021-10-22T23:41:42.096944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
29121
 
< 0.1%
28971
 
< 0.1%
28981
 
< 0.1%
29021
 
< 0.1%
29031
 
< 0.1%
29041
 
< 0.1%
29051
 
< 0.1%
29071
 
< 0.1%
29081
 
< 0.1%
Other values (2765)2765
99.6%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
56981
< 0.1%
56881
< 0.1%
56821
< 0.1%
56571
< 0.1%
56511
< 0.1%
56401
< 0.1%
56391
< 0.1%
56221
< 0.1%
56211
< 0.1%
56121
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2775
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15283.54811
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-22T23:41:42.190332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12625.7
Q113814.5
median15239
Q316779.5
95-th percentile17950.3
Maximum18287
Range5940
Interquartile range (IQR)2965

Descriptive statistics

Standard deviation1715.466409
Coefficient of variation (CV)0.1122426806
Kurtosis-1.206544394
Mean15283.54811
Median Absolute Deviation (MAD)1484
Skewness0.01769381525
Sum42411846
Variance2942825.001
MonotonicityNot monotonic
2021-10-22T23:41:42.281996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
138111
 
< 0.1%
169331
 
< 0.1%
137721
 
< 0.1%
162491
 
< 0.1%
141981
 
< 0.1%
139891
 
< 0.1%
179301
 
< 0.1%
144821
 
< 0.1%
141631
 
< 0.1%
Other values (2765)2765
99.6%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123631
< 0.1%
123641
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182651
< 0.1%
182631
< 0.1%
182611
< 0.1%
182601
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2761
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2826.841474
Minimum36.56
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-22T23:41:42.376763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum36.56
5-th percentile264.566
Q1627.06
median1166.77
Q32418.375
95-th percentile7395.585
Maximum279138.02
Range279101.46
Interquartile range (IQR)1791.315

Descriptive statistics

Standard deviation10426.6716
Coefficient of variation (CV)3.688452889
Kurtosis378.2513316
Mean2826.841474
Median Absolute Deviation (MAD)684.76
Skewness17.25283085
Sum7844485.09
Variance108715480.7
MonotonicityNot monotonic
2021-10-22T23:41:42.467258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1025.442
 
0.1%
889.932
 
0.1%
3312
 
0.1%
2053.022
 
0.1%
379.652
 
0.1%
731.92
 
0.1%
178.962
 
0.1%
1353.742
 
0.1%
745.062
 
0.1%
2092.322
 
0.1%
Other values (2751)2755
99.3%
ValueCountFrequency (%)
36.561
< 0.1%
521
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
70.021
< 0.1%
77.41
< 0.1%
84.651
< 0.1%
90.31
< 0.1%
93.351
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
70100.491
< 0.1%
66653.561
< 0.1%
65039.621
< 0.1%

recency_days
Real number (ℝ≥0)

ZEROS

Distinct251
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.60864865
Minimum0
Maximum372
Zeros33
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-22T23:41:42.564200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median29
Q373
95-th percentile210.3
Maximum372
Range372
Interquartile range (IQR)63

Descriptive statistics

Standard deviation68.23959812
Coefficient of variation (CV)1.205462412
Kurtosis3.437475511
Mean56.60864865
Median Absolute Deviation (MAD)24
Skewness1.895607805
Sum157089
Variance4656.642751
MonotonicityNot monotonic
2021-10-22T23:41:42.657689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.6%
487
 
3.1%
285
 
3.1%
385
 
3.1%
876
 
2.7%
1067
 
2.4%
966
 
2.4%
765
 
2.3%
1762
 
2.2%
2255
 
2.0%
Other values (241)2028
73.1%
ValueCountFrequency (%)
033
 
1.2%
199
3.6%
285
3.1%
385
3.1%
487
3.1%
543
1.5%
765
2.3%
876
2.7%
966
2.4%
1067
2.4%
ValueCountFrequency (%)
3721
 
< 0.1%
3661
 
< 0.1%
3601
 
< 0.1%
3583
0.1%
3541
 
< 0.1%
3371
 
< 0.1%
3362
0.1%
3341
 
< 0.1%
3332
0.1%
3301
 
< 0.1%

qnt_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.051171171
Minimum2
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-22T23:41:42.756279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median4
Q36
95-th percentile17
Maximum206
Range204
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.065784048
Coefficient of variation (CV)1.49818668
Kurtosis184.2954466
Mean6.051171171
Median Absolute Deviation (MAD)2
Skewness10.63311281
Sum16792
Variance82.18844041
MonotonicityNot monotonic
2021-10-22T23:41:42.855916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2781
28.1%
3499
18.0%
4393
14.2%
5237
 
8.5%
6173
 
6.2%
7138
 
5.0%
898
 
3.5%
969
 
2.5%
1055
 
2.0%
1154
 
1.9%
Other values (45)278
 
10.0%
ValueCountFrequency (%)
2781
28.1%
3499
18.0%
4393
14.2%
5237
 
8.5%
6173
 
6.2%
7138
 
5.0%
898
 
3.5%
969
 
2.5%
1055
 
2.0%
1154
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
621
< 0.1%
602
0.1%
571
< 0.1%

var_products
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct340
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.31927928
Minimum1
Maximum1786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-22T23:41:42.959331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q129
median57
Q3105
95-th percentile239.3
Maximum1786
Range1785
Interquartile range (IQR)76

Descriptive statistics

Standard deviation98.70446504
Coefficient of variation (CV)1.184653371
Kurtosis80.67285688
Mean83.31927928
Median Absolute Deviation (MAD)33
Skewness6.354212176
Sum231211
Variance9742.57142
MonotonicityNot monotonic
2021-10-22T23:41:43.054006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3738
 
1.4%
2437
 
1.3%
2636
 
1.3%
3335
 
1.3%
2834
 
1.2%
2533
 
1.2%
1832
 
1.2%
3032
 
1.2%
3130
 
1.1%
2330
 
1.1%
Other values (330)2438
87.9%
ValueCountFrequency (%)
119
0.7%
213
0.5%
317
0.6%
418
0.6%
522
0.8%
619
0.7%
721
0.8%
824
0.9%
923
0.8%
1020
0.7%
ValueCountFrequency (%)
17861
< 0.1%
17661
< 0.1%
13221
< 0.1%
11181
< 0.1%
8841
< 0.1%
8171
< 0.1%
7171
< 0.1%
7141
< 0.1%
6991
< 0.1%
6361
< 0.1%

qnt_items
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1636
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1664.05045
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-22T23:41:43.154881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile119.7
Q1330
median703
Q31478
95-th percentile4559.4
Maximum196844
Range196842
Interquartile range (IQR)1148

Descriptive statistics

Standard deviation5877.035531
Coefficient of variation (CV)3.531765236
Kurtosis489.6415784
Mean1664.05045
Median Absolute Deviation (MAD)451
Skewness18.27558807
Sum4617740
Variance34539546.64
MonotonicityNot monotonic
2021-10-22T23:41:43.253973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
2468
 
0.3%
1508
 
0.3%
12007
 
0.3%
2007
 
0.3%
2197
 
0.3%
5167
 
0.3%
2727
 
0.3%
4937
 
0.3%
2607
 
0.3%
Other values (1626)2699
97.3%
ValueCountFrequency (%)
21
< 0.1%
161
< 0.1%
171
< 0.1%
191
< 0.1%
201
< 0.1%
251
< 0.1%
272
0.1%
301
< 0.1%
321
< 0.1%
332
0.1%
ValueCountFrequency (%)
1968441
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%
502551
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct2773
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.49399163
Minimum2.150588235
Maximum1687.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-22T23:41:43.353651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.852950497
Q112.43090098
median17.94081081
Q324.99057143
95-th percentile86.23198132
Maximum1687.2
Range1685.049412
Interquartile range (IQR)12.55967045

Descriptive statistics

Standard deviation67.28411421
Coefficient of variation (CV)2.206471197
Kurtosis182.5219677
Mean30.49399163
Median Absolute Deviation (MAD)6.297941958
Skewness10.92852137
Sum84620.82676
Variance4527.152025
MonotonicityNot monotonic
2021-10-22T23:41:43.446572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.1622
 
0.1%
14.478333332
 
0.1%
18.152222221
 
< 0.1%
44.627692311
 
< 0.1%
19.030483871
 
< 0.1%
28.554516131
 
< 0.1%
12.800681821
 
< 0.1%
6.3962146891
 
< 0.1%
26.087971011
 
< 0.1%
17.984615381
 
< 0.1%
Other values (2763)2763
99.6%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%
615.751
< 0.1%
602.45313231
< 0.1%
591.70666671
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1155
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.7199783
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-22T23:41:43.806522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134.17361111
median59
Q399
95-th percentile224
Maximum366
Range365
Interquartile range (IQR)64.82638889

Descriptive statistics

Standard deviation66.48348631
Coefficient of variation (CV)0.8445567153
Kurtosis3.689451832
Mean78.7199783
Median Absolute Deviation (MAD)30
Skewness1.831366256
Sum218447.9398
Variance4420.053952
MonotonicityNot monotonic
2021-10-22T23:41:43.904030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7021
 
0.8%
4618
 
0.6%
5517
 
0.6%
3116
 
0.6%
4916
 
0.6%
9116
 
0.6%
4215
 
0.5%
2115
 
0.5%
3515
 
0.5%
2614
 
0.5%
Other values (1145)2612
94.1%
ValueCountFrequency (%)
19
0.3%
24
0.1%
2.8615384621
 
< 0.1%
36
0.2%
3.3303571431
 
< 0.1%
3.3513513511
 
< 0.1%
45
0.2%
4.1910112361
 
< 0.1%
4.2758620691
 
< 0.1%
4.51
 
< 0.1%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3641
 
< 0.1%
3631
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

freq_purchase
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1225
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04970841542
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-22T23:41:44.003672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.008746355685
Q10.01576875259
median0.0243902439
Q30.04166666667
95-th percentile0.1153846154
Maximum17
Range16.99455041
Interquartile range (IQR)0.02589791408

Descriptive statistics

Standard deviation0.3375327534
Coefficient of variation (CV)6.790253734
Kurtosis2297.378423
Mean0.04970841542
Median Absolute Deviation (MAD)0.0106974754
Skewness46.09420632
Sum137.9408528
Variance0.1139283596
MonotonicityNot monotonic
2021-10-22T23:41:44.099063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.062518
 
0.6%
0.0277777777817
 
0.6%
0.0238095238116
 
0.6%
0.0833333333315
 
0.5%
0.0909090909115
 
0.5%
0.0344827586215
 
0.5%
0.0294117647114
 
0.5%
0.0192307692313
 
0.5%
0.0212765957413
 
0.5%
0.0357142857113
 
0.5%
Other values (1215)2626
94.6%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%
1.1428571431
 
< 0.1%
18
0.3%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%
0.53
 
0.1%

qtd_returned
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct202
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.85333333
Minimum0
Maximum6504
Zeros1484
Zeros (%)53.5%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-22T23:41:44.196637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile94.3
Maximum6504
Range6504
Interquartile range (IQR)9

Descriptive statistics

Standard deviation217.5477318
Coefficient of variation (CV)7.051028472
Kurtosis413.3825127
Mean30.85333333
Median Absolute Deviation (MAD)0
Skewness17.99659879
Sum85618
Variance47327.01561
MonotonicityNot monotonic
2021-10-22T23:41:44.288858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01484
53.5%
1129
 
4.6%
2118
 
4.3%
382
 
3.0%
472
 
2.6%
663
 
2.3%
555
 
2.0%
1245
 
1.6%
839
 
1.4%
938
 
1.4%
Other values (192)650
23.4%
ValueCountFrequency (%)
01484
53.5%
1129
 
4.6%
2118
 
4.3%
382
 
3.0%
472
 
2.6%
555
 
2.0%
663
 
2.3%
738
 
1.4%
839
 
1.4%
938
 
1.4%
ValueCountFrequency (%)
65041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%
15941
< 0.1%
15352
0.1%

freq_returns
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct423
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2598677325
Minimum0
Maximum4
Zeros1484
Zeros (%)53.5%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-22T23:41:44.385690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2857142857
95-th percentile1
Maximum4
Range4
Interquartile range (IQR)0.2857142857

Descriptive statistics

Standard deviation0.4442600774
Coefficient of variation (CV)1.709562296
Kurtosis2.110364105
Mean0.2598677325
Median Absolute Deviation (MAD)0
Skewness1.487162012
Sum721.1329576
Variance0.1973670164
MonotonicityNot monotonic
2021-10-22T23:41:44.486003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01484
53.5%
1663
23.9%
210
 
0.4%
0.57
 
0.3%
0.28571428577
 
0.3%
0.025641025647
 
0.3%
0.256
 
0.2%
0.0094786729865
 
0.2%
0.019607843145
 
0.2%
0.012987012995
 
0.2%
Other values (413)576
 
20.8%
ValueCountFrequency (%)
01484
53.5%
0.0055710306411
 
< 0.1%
0.0056818181821
 
< 0.1%
0.0058651026391
 
< 0.1%
0.0059347181011
 
< 0.1%
0.0059523809521
 
< 0.1%
0.0060240963861
 
< 0.1%
0.0060422960731
 
< 0.1%
0.0061728395061
 
< 0.1%
0.0061919504641
 
< 0.1%
ValueCountFrequency (%)
41
 
< 0.1%
31
 
< 0.1%
210
 
0.4%
1663
23.9%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.57
 
0.3%
0.42857142861
 
< 0.1%
0.44
 
0.1%
0.33333333331
 
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1937
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean229.337363
Minimum1
Maximum3868.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-22T23:41:44.592276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45
Q1103.3333333
median172
Q3278.147619
95-th percentile583.45
Maximum3868.65
Range3867.65
Interquartile range (IQR)174.8142857

Descriptive statistics

Standard deviation237.5446348
Coefficient of variation (CV)1.035786894
Kurtosis45.2482469
Mean229.337363
Median Absolute Deviation (MAD)81
Skewness5.148880489
Sum636411.1823
Variance56427.45354
MonotonicityNot monotonic
2021-10-22T23:41:44.692279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
869
 
0.3%
608
 
0.3%
758
 
0.3%
1977
 
0.3%
1057
 
0.3%
2087
 
0.3%
1367
 
0.3%
827
 
0.3%
737
 
0.3%
Other values (1927)2697
97.2%
ValueCountFrequency (%)
11
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
11.8751
< 0.1%
ValueCountFrequency (%)
3868.651
< 0.1%
28801
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%
2082.2258061
< 0.1%
20001
< 0.1%
1903.51
< 0.1%
1866.9333331
< 0.1%
18581
< 0.1%

avg_basket_variety
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct897
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.14934231
Minimum0.2
Maximum177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-22T23:41:44.793284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.558441558
median13.5
Q322
95-th percentile45.075
Maximum177
Range176.8
Interquartile range (IQR)14.44155844

Descriptive statistics

Standard deviation14.25626243
Coefficient of variation (CV)0.8313008263
Kurtosis10.0170937
Mean17.14934231
Median Absolute Deviation (MAD)6.666666667
Skewness2.246725171
Sum47589.4249
Variance203.2410185
MonotonicityNot monotonic
2021-10-22T23:41:44.890089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
834
 
1.2%
1333
 
1.2%
932
 
1.2%
1632
 
1.2%
732
 
1.2%
1230
 
1.1%
1429
 
1.0%
629
 
1.0%
1729
 
1.0%
18.529
 
1.0%
Other values (887)2466
88.9%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333336
0.2%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.4%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
1771
< 0.1%
1051
< 0.1%
1041
< 0.1%
981
< 0.1%
95.51
< 0.1%
94.333333331
< 0.1%
93.333333331
< 0.1%
89.6251
< 0.1%
871
< 0.1%
85.666666671
< 0.1%

item_rp_ratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1238
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01593666388
Minimum0
Maximum1
Zeros1484
Zeros (%)53.5%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-22T23:41:44.992088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.008385781091
95-th percentile0.07096568749
Maximum1
Range1
Interquartile range (IQR)0.008385781091

Descriptive statistics

Standard deviation0.05860718928
Coefficient of variation (CV)3.677506769
Kurtosis71.20361791
Mean0.01593666388
Median Absolute Deviation (MAD)0
Skewness7.452081763
Sum44.22424226
Variance0.003434802636
MonotonicityNot monotonic
2021-10-22T23:41:45.089759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01484
53.5%
0.013605442183
 
0.1%
0.0024630541873
 
0.1%
0.0023094688223
 
0.1%
0.0074626865673
 
0.1%
0.02439024393
 
0.1%
0.012295081973
 
0.1%
0.014925373133
 
0.1%
0.0096618357493
 
0.1%
0.0037735849062
 
0.1%
Other values (1228)1265
45.6%
ValueCountFrequency (%)
01484
53.5%
0.00011696362431
 
< 0.1%
0.00018399264031
 
< 0.1%
0.00028169014081
 
< 0.1%
0.00031407035181
 
< 0.1%
0.00036192544341
 
< 0.1%
0.00036324010171
 
< 0.1%
0.00036376864311
 
< 0.1%
0.00036710719531
 
< 0.1%
0.0003930817611
 
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.63333333331
< 0.1%
0.60088365241
< 0.1%
0.59645669291
< 0.1%
0.56488549621
< 0.1%
0.56463878331
< 0.1%
0.56020408161
< 0.1%
0.53990610331
< 0.1%
0.53321033211
< 0.1%
0.52083333331
< 0.1%

net_margin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1295
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.979610587
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-22T23:41:45.187203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.9063504779
Q10.9846235941
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.01537640594

Descriptive statistics

Standard deviation0.06051134253
Coefficient of variation (CV)0.06177081315
Kurtosis66.77619311
Mean0.979610587
Median Absolute Deviation (MAD)1.110223025 × 10-16
Skewness-6.846425452
Sum2718.419379
Variance0.003661622575
MonotonicityNot monotonic
2021-10-22T23:41:45.283026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11337
48.2%
159
 
2.1%
145
 
1.6%
143
 
1.5%
0.98097273151
 
< 0.1%
0.99639832891
 
< 0.1%
0.95806228951
 
< 0.1%
0.99961497151
 
< 0.1%
0.53578682871
 
< 0.1%
0.98232794061
 
< 0.1%
Other values (1285)1285
46.3%
ValueCountFrequency (%)
01
< 0.1%
0.14017054081
< 0.1%
0.25023409121
< 0.1%
0.35486018641
< 0.1%
0.481
< 0.1%
0.48960788521
< 0.1%
0.51552650011
< 0.1%
0.52021552081
< 0.1%
0.53017349081
< 0.1%
0.53465812861
< 0.1%
ValueCountFrequency (%)
145
 
1.6%
11337
48.2%
159
 
2.1%
143
 
1.5%
0.99991807691
 
< 0.1%
0.99984316131
 
< 0.1%
0.99972431321
 
< 0.1%
0.99967287611
 
< 0.1%
0.99961497151
 
< 0.1%
0.99954246981
 
< 0.1%

Interactions

2021-10-22T23:41:40.213680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:18.059571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:19.449644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:20.951234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:22.415380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:23.863852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:25.437205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:26.852105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:28.333823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:29.898140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:31.340059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:32.934915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:34.324738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:35.774331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:37.250763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:38.869239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:40.297374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:18.164162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:19.530245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:21.030047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:22.506060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:23.958778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:25.520623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:26.940278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:28.418595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:29.982582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:31.423207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:33.023880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:34.410010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:35.861862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:37.338149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:38.950393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:40.380680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:18.262304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:19.610336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:21.110207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:22.593545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:24.045925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:25.603970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:27.028482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:28.647279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:30.066420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:31.507989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:33.108339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:34.495948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:35.950319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:37.426027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:39.031126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:40.463208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:18.343420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:19.690530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:21.189340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:22.681865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:24.131355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:25.688428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:27.117537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:28.730773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:30.150605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:31.592580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T23:41:34.952591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:36.415158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T23:41:39.457733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:40.898003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:18.771289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T23:41:29.271107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:30.694462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:32.136379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:33.717252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:35.133467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:36.598922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T23:41:21.797553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:23.323535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:24.776247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:26.316322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:27.779034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:29.360125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:30.785385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T23:41:41.155254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T23:41:21.882520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:23.411752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:24.865171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:26.404017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:27.869508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:29.446690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:30.871960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:32.315397image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:33.887797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:35.313115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:36.784550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T23:41:39.792533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:41.246124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:19.114770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:20.577450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:21.973322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:23.503315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T23:41:35.407542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T23:41:39.880026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:41.336332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T23:41:20.668849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:22.062030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:23.596563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:25.171800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:26.590617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T23:41:31.058228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T23:41:35.505664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:36.978058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:38.611604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:39.967259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:41.424091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T23:41:29.726614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:31.147902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:32.591878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:34.158200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T23:41:40.051750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T23:41:19.365644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:20.863119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-22T23:41:23.776108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:25.348938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:26.764800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:28.246924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:29.813077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:31.234432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:32.676914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:34.241174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:35.686257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:37.161429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:38.784645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-22T23:41:40.131757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-10-22T23:41:45.377752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-22T23:41:45.524822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-22T23:41:45.672940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-22T23:41:45.821570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-22T23:41:41.672585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-22T23:41:41.869857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqnt_purchasesvar_productsqnt_itemsavg_ticketavg_recency_daysfreq_purchaseqtd_returnedfreq_returnsavg_basket_sizeavg_basket_varietyitem_rp_rationet_margin
00178505391.21372.034.021.01733.018.1522221.00000017.00000040.01.00000050.9705880.6176470.0230810.980973
11130473232.5956.09.0105.01390.018.90403552.8333330.02830235.00.023973154.44444411.6666670.0251800.955611
22125836705.382.015.0114.05028.028.90250026.5000000.04032350.00.105263335.2000007.6000000.0099440.988660
3313748948.2595.05.024.0439.033.86607192.6666670.0179210.00.00000087.8000004.8000000.0000001.000000
4415100876.00333.03.01.080.0292.00000020.0000000.07317122.00.07894726.6666670.3333330.2750000.725000
55152914623.3025.014.061.02102.045.32647126.7692310.04011529.00.032468150.1428574.3571430.0137960.984472
66146885630.877.021.0148.03621.017.21978619.2631580.057221399.00.019608172.4285717.0476190.1101910.907032
77178095411.9116.012.046.02057.088.71983639.6666670.03352041.00.013072171.4166673.8333330.0199320.987609
881531160767.900.091.0567.038194.025.5434644.1910110.243316474.00.072193419.7142866.2307690.0124100.977808
99160982005.6387.07.034.0613.029.93477647.6666670.0243900.00.00000087.5714294.8571430.0000001.000000

Last rows

df_indexcustomer_idgross_revenuerecency_daysqnt_purchasesvar_productsqnt_itemsavg_ticketavg_recency_daysfreq_purchaseqtd_returnedfreq_returnsavg_basket_sizeavg_basket_varietyitem_rp_rationet_margin
2765561217290525.243.02.092.0404.05.14941213.00.1428570.00.0202.00000046.0000000.0000001.000000
276656211478577.4010.02.02.084.025.8000005.00.3333330.00.042.0000001.0000000.0000001.000000
2767562217254272.444.02.0100.0252.02.43250011.00.1666670.00.0126.00000050.0000000.0000001.000000
2768563917232421.522.02.030.0203.011.70888912.00.1538460.00.0101.50000015.0000000.0000001.000000
2769564017468137.0010.02.05.0116.027.4000004.00.4000000.00.058.0000002.5000000.0000001.000000
2770565113596697.045.02.0133.0406.04.1990367.00.2500000.00.0203.00000066.5000000.0000001.000000
27715657148931237.859.02.072.0799.016.9568492.00.6666670.00.0399.50000036.0000000.0000001.000000
2772568214126706.137.03.014.0508.047.0753333.00.75000050.01.0169.3333334.6666670.0984250.911489
27735688135211092.391.03.0312.0733.02.5112414.50.3000000.00.0244.333333104.0000000.0000001.000000
2774569815060301.848.04.080.0262.02.5153331.02.0000000.00.065.50000020.0000000.0000001.000000